Trend Analysis of Flood Peaks in Lower Reaches of Satluj

Trend analysis of flood peaks in lower reaches of Satluj River, Himachal Pradesh, India

*Sandeep Kumar1, Santosh2

1Department of Environment Studies, Panjab University, Chandigarh, India.

2Department of Environmental Sciences, MDU, Rohtak (Haryana), India.

E-mail address: [email protected]; [email protected]

Keywords: Flood peaks, Mann-Kendall test, Satluj River Basin, Regression, Trend analysis.

ABSTRACT. Climate change arising from anthropogenic driven emissions of greenhouse gases

has emerged as one of the most important environmental issues in the last two decades. One of the

most significant potential consequences of climate change may be alteration in regional

hydrological cycle and river flow regimes. Increased temperature is expected to increase the peak

flows in snow-fed rivers of Himalayas. The changing pattern of regional temperature on flood peaks

deserves urgent and systematic attention over a basin which provides an insight view of historical

trends. Lower reaches of Satluj River is selected for the present study. Testing the significance of

observed trends in flood peaks has received a great attention recently, especially in connection with

climate change. The data series available was 48 years (1967-2010). The records were subjected to

trend analysis by using both non-parametric (Mann-Kendall test) and parametric (linear regression

analysis) procedures. For better understanding of the observed trends, flood peaks were computed

into standardised flood peak indices (SFPI). These standardised data series were plotted against

time and the linear trends observed were represented graphically. The analysis of flood peaks at

different observation stations in lower reaches of Satluj River showed a large variability in the

trends and magnitudes. The trend analysis results of flood peaks and gauge heights indicate that the

flood peaks at all sites i.e. Rampur, Suni and Kasol show increasing but statistically insignificant

trends. The trends in gauge height at all sites are also showing increasing trend but Kasol is

statistically significant at 95% confidence level. The fast melting of glaciers, incessant monsoon

rainfall and the synchronisation of the discharge peaks are the main causes of river floods. The past

flood peaks will help us to observe the frequency of occurrence of floods in certain region and to

determine whether the flood peaks in the past have been same with that of the present or whether

there is any deviation in the trend in relation to climate change. Such studies will help in designing

mitigation and adaptation strategies towards extreme hydrological events.

1. INTRODUCTION

Climate change arising from anthropogenic driven emissions of greenhouse gases has

emerged as one of the most important environmental issues in the last two decades. Information

about impacts of climate change is required at global, regional and basin scales for a variety of

purposes. The spatial distribution and magnitude of temperature trends would help in obtaining

climate change scenarios. It may have implications on various environmental variables. The

consequences of climate change on Indian sub-continent, especially the mountainous regions are

poorly understood. Its impacts will have significant effect on the northwest region of India. The

Himalayan region has large and intricate network of river systems which is reinforced by the

snowmelt, glaciermelt and rainfall. Many of major rivers originating in this region have their upper

catchments covered by snow/glacier. The altitude and climatic change induced precipitation

variability are expected to play major role on discharge pattern of snow-fed rivers of Himalayas,

coupled with an increase in peak flows.

An understanding of the hydrological response of a river basin under changing climatic

conditions will help to resolve potential issues associated with hydro-meteorological disasters and

availability of water for agriculture, industry, hydropower, domestic use etc. The changing pattern

International Letters of Natural Sciences Online: 2015-09-11ISSN: 2300-9675, Vol. 46, pp 60-75doi:10.18052/www.scipress.com/ILNS.46.60CC BY 4.0. Published by SciPress Ltd, Switzerland, 2015

This paper is an open access paper published under the terms and conditions of the Creative Commons Attribution license (CC BY)(https://creativecommons.org/licenses/by/4.0)

https://doi.org/10.18052/www.scipress.com/ILNS.46.60

of discharge and flood peaks deserves urgent and systematic attention over a basin which provides

an insight view of historical trends. Therefore, information on trend analysis over a basin scale is of

utmost importance for planning, development and utilisation of water resources. Trend analysis has

proved to be useful tool for effective water resources planning, design and management since trend

detection of hydrological variables provides a useful information on the possibility of change in

tendencies of the variables in future (Hamilton et al., 2001; Yue and Wang, 2004).

The intensive human activities, including reservoir construction, hydropower development

and water abstraction have become more serious issues from the upper to the lower part of the

Satluj River Basin. These have substantially changed the hydrology of the river. Extreme climatic

events often cause catastrophic damage to nature and human society. Therefore, regional

assessments of various climates and geographic regions are needed for understanding the

uncertainties of the changing trends for extreme climatic events (Li et al., 2010). One of the most

significant potential consequences of climate change may be alteration in regional hydrological

cycle and river flow regimes. Necessity of the hour is to concentrate on the studies, how the

possible climate change will affect the intensity, temporal and spatial variability of flood peaks in

river basins.

Therefore, one large catchment (Satluj) is selected for the present study. Most of the flow in

its upper mountain basin is derived from the melting of snow and glaciers. Climatic variability and

change in precipitation pattern will affect the flooding pattern in the river. The changes in the

amount and seasonal distribution of precipitation have potentially serious implications for the

hydrological regimes of catchment area. The nature of climate change and its impact on the

hydrology of the Satluj River Basin have been studied inadequately because of inaccessibility,

terrain ruggedness and sparse network of gauge stations. The present study tries to fill this vacuum

by studying the trend analysis of flood peaks.

Testing the significance of observed trends in hydrological and meteorological time series has

received a great attention recently, especially in connection with the assessment of observed

changes in the natural and human environment due to global warming. This is reflected by a huge

number of studies carried out over the last three decades, dealing with the assessment of

significance of trends in flood peaks.

Changes in flood frequencies are expected at some locations, particularly in northern latitudes

and in catchments experiencing snowmelt flooding events (Burn and Hag Elnur, 2002). The spatial

and temporal patterns in the annual maximum and minimum water level in the Pearl River Delta

(PRD) region were studied by Zhang et al. (2009). There is no obvious trend for the annual

maximum water level in the upper region, while the stations in the middle region exhibit an

increasing trend and this upward trend is significant in the lower region. Some stations in the upper

part showed increasing trend for the annual extreme runoff. Apart from the climatic changes, human

activities are also responsible for the temporal and spatial variability of annual extreme water levels.

Peak flows due to rainfall events in the summer are increasing, as well as the number of days with

higher flows are increasing (Novotny and Stefan, 2007).

A sensitivity study by Cunderlik and Simonovic (2005) for a catchment in Ontario, Canada,

projected a decrease in snowmelt induced floods, while an increase in rain induced floods is

anticipated. The variability of annual maximum flow is projected to increase. For the recent global

warming of the 20th

century, no general and coherent trends could be observed with regard to

increases in annual maximum flows (Kundzewicz et al., 2005). However, the increasing risk of

floods was detected in 29 basins larger than 20,000 Km2

(Milly et al., 2002). Yet, in some highly

vulnerable regions, a significant increase in flooding probabilities has been found under global

warming (Mirza, 2002). The variability in extreme discharges for climate change conditions

increases with respect to the simulations for current climate conditions. The total uncertainty in

river flooding with climate change is much larger than the change with respect to current climate

conditions (Booij, 2004).

The future climate would be wetter and warmer than the present climate affecting the type,

magnitude and temporal distribution of floods as well as the frequency of flood peaks (Loukas et

International Letters of Natural Sciences Vol. 46 61

al., 2004). The frequency of great floods increased substantially during the twentieth century. The

recent emergence of a statistically significant positive trend in risk of great floods is consistent with

results from the climate model, and the model suggests that the trend will continue (Milly et al.,

2002). Peak flow in the rivers is likely to move from spring to winter in many areas due to early

snowmelt, with lower flows in summer and autumn. Glacier retreat is likely to continue and many

small glaciers may disappear in future. Rapid melting of glaciers can lead to flooding of rivers and

to the formation of glaciermelt water lakes which may pose a serious threat of outburst floods

(Coudrain et al., 2005). There is increase in frequency and intensity of extreme hydro-

meteorological events (Viviroli et al., 2010).

Annual peak flood discharges in the Satluj and Chenab Basins showed significant increasing

trends; significant decreasing in Beas River and insignificant trend in Ravi River. Notwithstanding

these variations, there is increase in the number of high magnitude flood events in the northwestern

Himalaya Rivers during the last three decades. The results of the trend analysis of Standardised

Discharge Indices (SDI) of annual peak flood discharges of the four rivers of the North Western

Himalayas (NWH) show that the Satluj and Chenab rivers reveal significant increasing trends

while Ravi and Beas show insignificant increasing and significant decreasing trends respectively, at

95% confidence level. This means that only the Satluj and Chenab appear to have a response

matching with the variation in temperature (Bhutiyani et al., 2008). Jiang et al. (2007) concluded a

positive trend in flood discharges in most of stations in the Yangtze River Basin, China.

2. STUDY AREA

The Satluj River (Vedic name - Satudri and Sanskrit name - Shatadru), also known as the

Langqên (Chinese) and Sutlej (Indian), is the principal and easternmost tributary of the Indus River

system. The basin area falls in Lahaul & Spiti, Kinnaur, Shimla, Kullu, Mandi, Solan and Bilaspur

districts of Himachal Pradesh. The geographical limits of area lie between 30°45′ N to 33°00′ N

latitudes and 76°15′ E to 79°00′ E longitudes in the western Himalayas (Figure 1). The total

catchment area of Satluj River, from origin to Bhakra dam, is about 56,875 km2

(21,960 Sq. miles).

The upper part of river basin is considerably wider than the lower one. In Himachal Pradesh, Satluj

Basin has catchment area of 20,398 Km2

which is 30.7% of the total catchment area of river systems

(SCST & E, 2006). Indian part of river up to Bhakra Dam is elongated in shape and covers the part

of outer (Shiwalik range), middle (Dhauladhar range) and greater Himalayas (Zaskar range).

Satluj River originates from the southern slopes of Kailash Mountains i.e. from Rakas Lake,

near the Mansarovar Lake as Longcchen Khabab River at an elevation of about 4,572 m (15,000 ft),

above msl. Total length of river is approximately 1,448 km (320 Km in China, 758 Km in India and

370 Km in Pakistan). It enters India from East of Shipki La (altitude – 3,048 m, above msl) after

traversing a length of about 320 km (200 miles) in the Tibetan province of Nari Khorsam, through a

narrow gorge in the Kinnaur district of Himachal Pradesh and flows in southwesterly direction. The

river is supported by a number of mighty tributaries on either side. Main tributaries are Spiti, Baspa

and Gambhar at Khab, Karchham and Kangri at an elevation of 2,600, 1,750 and 450 m above msl

respectively. Near Rampur, it crosses the Dhauladhar range and then traverses through a series of

successive Shiwalik ranges. Before leaving the Himachal Pradesh, it cuts a gorge in Naina Devi

Dhar and mingles with the water of Govind Sagar Lake. It enters the plains of Punjab near Bhakra

where Asia’s one of the highest gravity multipurpose dam (Capacity to generate electricity -1,325

MW and height - 740 ft/225.55 m) has been constructed. It finally drains into the main Indus River

in Pakistan.

Based on the amount of annual precipitation and the variation in temperature, the study area,

from North to South, has been divided into three broad climatic zones (Figure 2). Each zone is

characterised by its own peculiarities of climatic factors, geomorphic and topographic features

(Gupta et al., 1994; Bartarya et al., 1996):

1. Semi-arid to arid temperate zone (Cold desert) - This zone lies in the upper Satluj Valley,

upstream from Morang. Towards North of Morang, the cold desert conditions prevail which

62 ILNS Volume 46

are characterised by very low monsoonal precipitation, high speed of cold winds and the

precipitation generally occurs in the form of snowfall during winter season.

2. Sub-humid to humid temperate zone - This zone covers the middle Satluj Valley between

the Wangtu and Morang. It is the transitional zone which receives low rainfall during the

monsoon period and moderate to heavy snowfall in the higher reaches during winter.

3. Wet temperate or Monsoonal zone - It lies in the lower Satluj Valley downstream of

Wangtu. This zone is under the great influence of monsoonal winds and receives heavy

rainfall during rainy season from mid June to mid September.

Figure 1. Schematic showing the study area map of Satluj River Basin upto Bhakra Dam, Himachal Pradesh.


Figure 2. Longitudinal profile of Satluj River from Shipki La to Bhakra Dam. Three climatic zones are

demarcated along with the major thrusts.

The fall of Satluj from its source to the plain areas is very uniform. A gross fall of 2,180 m is

available in the river bed from Shipki La to Bhakra in a length of about 320 Km (Figure 2). The

altitude in the study area increases from West to East and South to North. Based on broad climatic

conditions, the Satluj River Basin has following four seasons: Winter (December to March), Pre-

monsoon (April to June), Monsoon (July to September), Post-monsoon (October, November).

3. MATERIALS AND METHODS

For the considered study area, records of flood peaks and gauge heights were subjected to

trend analysis by Mann-Kendall and regression coefficient test. In order to determine the degree and

rate of change in such variables, long-term data sets are required. Burn and Elnur (2002) stated that

a minimum record length of 25 years ensures validity of the trend results statistically. The present

analytical study on the spatial and temporal trends is based on the available hydrological data from

Bhakra Beas Management Board (BBMB), Nangal. The trends were identified by investigating the

time series of different observation stations distributed over the lower reaches of Indian part of

Satluj River. Data was scarce especially in the upper catchment area because of inadequate hydro-

meteorological networks in the high altitude regions with rugged terrain and poor accessibility. The

data length available was 48 years for flood peaks (cumecs) and gauge heights (Feet).

In order to investigate trends in time series, observational records were prepared. For flood

peaks, the data series were directly available. To bring uniformity and facilitate comparison,

standardised flood peak indices (SFPI) can be computed by subtracting the mean and dividing by

the standard deviation of discharge data series. The SFPI data series are then subjected to trend

analysis by statistical techniques (Pant and Rupakumar, 1997; Shreshtha et al., 2000; Bhutiyani et

al., 2008). These standardised time series data were plotted against time and the linear trends

observed were represented graphically. The linear trend values, represented by the slope of a simple

least square regression line with time as independent variable gave the magnitude of rise or fall.

Univariate (flood peaks and gauge height) as well as multivariate (annual and monsoonal discharge

vs flood peaks) trends were studied.

The location (longitude and latitude) and altitude of flood peak observation sites are shown in

Table 1 and figure 3. The altitude of the hydro-meteorological stations varied from 809 to 1,302 m.

Shipki La

Khab (confluence with Spiti)

Morang

Wangtu

Rampur

Bilaspur Bhakra

0

500

1000

1500

2000

2500

3000

0 40 80 120 160 200 240 280 320

Ap

pro

x.

river

bed

hei

ght

(m,

abo

ve

msl

)

Distance (Km)

Tethyan Himalaya

Higher Himalaya

Lesser Himalaya

Semi arid-arid temperate

Sub humid-humid temperate

Wet temperate

Tethyan Thrust Vaikrita Thrust

Karchham Thrust

Main Central Thrust

Jutogh Thrust

Chail Thrust

64 ILNS Volume 46

Table 1. Flood peak data availability in Satluj River Basin.

Station Latitude and longitude Elevation (m)

Rampur 3127'15" & 7738'40" 1,302

Suni 3114'15" & 7706'30" 843

Kasol 3121'25" & 7652'42" 809

Figure 3. Location of flood peak observation sites in lower reaches of Satluj River Basin.

In order to investigate the trends, several statistical techniques are currently available. In the

present study, trend analysis have been made by using both non-parametric (Mann-Kendall test) and

parametric (linear regression analysis) procedures. Parametric tests assume that the random variable

is normally distributed and homo-sedastic (homogeneous variance). Non-parametric tests make no

assumption for probability distribution. The purpose of trend analysis is to determine if the values

of a random variable generally increase (or decrease) over some period of time in statistical terms

(Helsel and Hirsch, 1992).

3.1. Mann-Kendall test

The non-parametric tests are more suitable for non-normally distributed, censored data,

including missing values which are frequently encountered in hydro-meteorological time series

(Hirsch and Slack, 1984; Yue and Pilon, 2004). The Mann-Kendall trend test has therefore been

widely used for testing trends in many natural time series that deviate significantly from the Normal

distribution, such as temperature. The MK test was found to be an effective tool for identifying

trends in hydrologic and other related variables, resistant to the effect of extreme values (Hirsch et

al., 1982; Burn, 1994). This test has been applied to temperature, precipitation and stream flow data

series (Yu et al., 1993; Douglas et al., 2000; Yue et al., 2003; Burn et al., 2004). The MK test

applied in this study is a rank based method for evaluating the presence of trends in time series data,

without specifying whether the trend is linear or nonlinear.

To identify the trends in the climatic variables with reference to climate change, the non-

parametric Mann-Kendall test (Mann, 1945; Kendall, 1975) has been applied in hydro-

meteorological data. Mann originally used this test and Kendall subsequently derived the test for


statistical distribution. The co-variances between Mann-Kendall statistics were proposed by Dietz

and Kileen (1981) and the test was extended in order to include seasonality (Hirsch and Slack,

1984). The test compares the relative magnitudes of sample data rather than the data values

themselves (Gilbert, 1987). In the present study, test was applied to mean temperature for

determining monotonic trends.

The MK test has two parameters i.e. significant level, indicates the trend’s strength and the

slope magnitude, indicates the direction as well as magnitude of the trend. The data values are

evaluated as an ordered time series. Each data value is compared to all subsequent data values. The

initial value of the Mann-Kendall statistic, S, is assumed to be 0 (e.g., no trend). If a data value from

a later time period is higher than a data value from an earlier time period, S is incremented by 1. On

the other hand, if the data value from a later time period is lower than a data value sampled earlier,

S is decremented by 1. The net result of all such increments and decrements yields the final value of

S. A very high positive value of S is an indicator of an increasing trend and a very low negative

value indicates a decreasing trend. However, it is necessary to compute the probability associated

with S and the sample size, n, to statistically quantify the significance of the trend. So, the MK test

considers only the relative values of all terms in the series X = {x1, x2,….,xn} to be analysed. The

Mann-Kendall statistic S which is the sum of the differences between the data points is defined as

(Salas, 1992)

where xi and xj are the sequential data values and n is the number of data points. Let xj - xi = θ.

The value of sign (θ) is computed as

For large samples (n > 10), the test is conducted using a normal distribution (Helsel and Hirsch,

1992) with mean E(S) and variance Var (S). Kendall (1975) obtained the theoretical mean and

variance of S under the assumption of no trend as:

where tk is the extent of any given tie (number of x’s involved in a given tie), and Σtk denotes the

sum of the terms tk(tk – 1)(2tk + 5) which are evaluated and summed for each tie of the tk number in

the data. The standard normal variable Z is computed by:

66 ILNS Volume 46

Compute the probability associated with this normalised test statistic. The trend is said to be

decreasing if Z is negative and the computed probability is greater than the level of significance. If

the q-value is less than or equal to the significance level then it is correct to reject the null

hypothesis that a trend does not exist in the data set. The trend is said to be increasing if the Z is

positive and the computed probability is greater than the level of significance. If the computed

probability is less than the level of significance, there is no trend. If the computed value of |Z| > z

α/2, the null hypothesis Ho of having no trend in the data series is rejected at α level of significance

in a two-sided test. Thus, in a two-tailed test for trend, the null hypothesis, that there is no trend in

the dataset, is either rejected or accepted depending on whether the calculated Z statistics is more

than or less than the critical value of Z-statistics obtained from the normal distribution table at 5%

significance level. Significance levels (p-values) for each trend test can be obtained as:

p = 2[1-Φ|Z|]

where Φ denotes the cumulative distribution function (cdf) of a standard normal variant.

3.2. Pre-Whitney

However, a basic assumption for the original Mann-Kendall test is that the data should be

random and identically distributed which is seldom the case in natural time series. It has been long

known (Cox and Stuart, 1955) that positive serial correlation among the observations would

increase the chance of a significant answer even in the absence of a trend. The presence of serial

correlation can complicate the identification of trends that a positive serial correlation can

increase the expected number of false positive outcomes for MK test (Von Storch and Navarra,

1995). So, before applying the MK test, the data series was tested for serial correlation. In order to

limit the influence of serial correlation, pre-whitening was proposed by Von Storch (1995). Bayazit

and Önöz (2007) suggested that pre-whitening should be avoided when the test has a high power,

the slope of trend is high, and the sample size is large (i.e., n ≥ 50).

In present study, Mann-Kendall test was used in conjunction with the widely used method of

pre-whitening. If the lag -1 auto-correlation r1 was found to be non-significant at 95% confidence

level, then the MK test was applied directly to the original data series x1, x2, . . . ,xt. Otherwise, the

test was applied to the pre-processed data series x2 - r1x1, x3 - r1x2, . . . ,xt - r1xt-1 referred to as ‘pre-

whitened’ (Von Storch and Navarra, 1995; Partal and Kahya, 2006). The pre-whitening removes

serial correlation from the data by means of the following formula:

t = xt - r1xt-1

where xt is the original time series value for time interval t, t is the pre-whitened time series

value and r1 is the lag -1 autocorrelation coefficient that can be expressed as:

where E(xt) is the mean of the sample data. Von Storch and Navarra (1995) also demonstrated that

pre-whitening operation is not necessary for r1 ≤ 0.1.

3.3. Regression

The changes in flood peaks were plotted against time and the trend was examined by fitting the

linear regression line. Linear regression analysis indicates the tendency rate (slope) using least

squares at the 95% confidence level, indicates the mean temporal change of the studied variable.

Positive values of the slope show increasing trends, while negative values indicate decreasing

trends. The total change during the period under observation is obtained by multiplying the slope

with the number of years (Tabari and Marofi, 2010; Tabari et al., 2010a, b). The parametric test

considers the linear regression of the random variable Y on X, expressed as:

Y = β0 + β1X + ε


4. Results and discussion

The analysis of flood peaks of different observation sites in the basin showed a large

variability in the trends and magnitudes. Figures (4-12) and Tables 2 show the results of trend

analysis of flood peaks and gauge height at lower region of the river basin, for time-span of 48

years (1963-2010). Investigation of the relationship between trends in hydrological variables was

also carried out i.e. annual as well as monsoonal discharge and flood peaks.

Figure 4. Time series plot for annual flood peaks and maximum gauge height at Rampur.

Figure 5. Comparison of annual discharge with flood peaks at Rampur (*Significant at 95% confidence

level).

y = 6,9591x + 1476,6

R² = 0,0166

y = 0,0454x + 19,94

R² = 0,0194

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

40,00

45,00

0

1000

2000

3000

4000

5000

6000

Ga

ug

e h

eig

ht

(Feet)

Flo

od

pea

ks

(Cu

mecs)

Date/month/year

Annual flood peaks Vs maximum gauge height at Rampur

Flood peaks in cumecs Maximum gauge in feet Linear trend (Flood peaks) Linear trend (Maximum gauge)

y = -3.776x + 378.7

R² = 0.228*

y = 4,8959x + 1612,8

R² = 0,0017

0

1000

2000

3000

4000

5000

6000

0

50

100

150

200

250

300

350

400

450

500

Flo

od

pea

ks

(Cu

mecs)

An

nu

al

dis

cha

rg

e (

Cu

mecs)

Year

Annual discharge Vs flood peaks at Rampur

Annual discharge Flood peaks Linear trend (annual discharge) Linear trend (flood peaks)

68 ILNS Volume 46

Figure 6. Comparison of monsoonal discharge with flood peaks at Rampur (*Significant at 95% confidence

level).

The analysis at Rampur site of Satluj River (Figure 4-6) shows that flood peaks and gauge

heights are corresponding to each other and have increasing but statistically insignificant trends. A

comparison of annual and monsoonal average discharge with flood peaks was done. Annual average

discharge shows the decreasing trend which is statistically significant, while increasing but

statistically insignificant trend was shown by flood peaks. Similarly, monsoonal average discharge

shows the statistically significant decreasing trend, while the flood peak analysis indicates

increasing but statistically insignificant trend.

Figure 7. Time series plot for annual flood peaks and maximum gauge height at Suni.

y = -7.713x + 769.1

R² = 0.243*

y = 4,8959x + 1612,8

R² = 0,0017

0

1000

2000

3000

4000

5000

6000

0

100

200

300

400

500

600

700

800

900

1000

Flo

od

pea

ks

(Cu

mecs)

Mo

nso

on

al d

isch

arg

e (

Cu

mecs)

Year

Monsoonal discharge Vs flood peaks at Rampur

Monsoonal discharge Flood peaks Linear trend (monsoonal discharge) Linear trend (flood peaks)

y = 14,379x + 1632

R² = 0,0481

y = 0,0804x + 25,025

R² = 0,0309

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

0

1000

2000

3000

4000

5000

6000

7000

Ga

ug

e h

eig

ht

(Feet)

Flo

od

pea

ks

(Cu

mecs)

Date/month/year

Annual flood peaks Vs maximum gauge height at Suni

Flood peaks in cumecs Maximum gauge in feet Linear trend (Flood peaks) Linear trend (Maximum gauge)


Figure 8. Comparison of annual discharge with flood peaks at Suni (*Significant at 95% confidence level).

Figure 9. Comparison of monsoonal discharge with flood peaks at Suni (*Significant at 95% confidence

level).

The analysis at Suni site of Satluj River (Figure 7-9) shows that flood peaks and gauge heights

are corresponding to each other and have increasing but statistically insignificant trend. A

comparison of annual and monsoonal average discharge with flood peaks was done. Annual average

discharge shows the decreasing trend which is statistically significant, while increasing but

statistically insignificant trend was shown by flood peak. Similarly, monsoonal average discharge

shows the statistically significant decreasing trend, while the flood peak analysis indicates

increasing but statistically insignificant trend.

y = -2.629x + 407.8 R² = 0.124*

y = 23.36x + 1770 R² = 0.027

0

1000

2000

3000

4000

5000

6000

7000

0

50

100

150

200

250

300

350

400

450

500

Flo

od

pea

ks

(Cu

mecs)

An

nu

al

dis

cha

rg

e (

Cu

mecs)

Year

Annual discharge Vs flood peaks at Suni

Annual discharge Flood peaks Linear trend (annual discharge) Linear trend (flood peaks)

y = -3.743x + 839.5

R² = 0.042*

y = 23.36x + 1770

R² = 0.027

0

1000

2000

3000

4000

5000

6000

7000

0

200

400

600

800

1000

1200

Flo

od

pea

ks

(Cu

mecs)

Mo

nso

on

al d

isch

arg

e (

Cu

mecs)

Year

Monsoonal discharge Vs flood peaks at Suni


70 ILNS Volume 46

Figure 10. Time series plot for annual flood peaks and maximum gauge height at Kasol (*Significant at 95%

confidence level).

Figure 11. Comparison of annual discharge with flood peaks at Kasol.

y = 13,537x + 1919,2

R² = 0,0287

y = 0.31x + 35.94

R² = 0.209*

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

0

1000

2000

3000

4000

5000

6000

7000

8000

Ga

ug

e h

eig

ht

(Feet)

Flo

od

pea

ks

(Cu

mecs)

Date/month/year

Annual flood peaks Vs maximum gauge height at Kasol

Flood peaks in Cumecs Maximum gauge in feet Linear trend (Flood peaks) Linear trend (Maximum gauge)

y = 1,2413x + 404,83

R² = 0,0188

y = 21,383x + 2087,3

R² = 0,0165

0

1000

2000

3000

4000

5000

6000

7000

8000

0

100

200

300

400

500

600

700

Flo

od

pea

ks

(Cu

mecs)

An

nu

al

dis

cha

rg

e (

Cu

mecs)

Year

Annual discharge Vs flood peaks at Kasol

Annual discharge Flood peaks


Figure 12. Comparison of monsoonal discharge with flood peaks at Kasol.

The analysis at Kasol site of Satluj River (Figure 10-12) shows that flood peaks and gauge

heights are corresponding to each other and have increasing trend but the trend is significant in

gauge height and insignificant in flood peaks. A comparison of annual and monsoonal average

discharge with flood peaks was done. Annual average discharge and flood peaks show increasing

trends which are statistically insignificant. The monsoonal average discharge shows the statistically

insignificant decreasing trend, while the flood peak analysis indicates increasing but statistically

insignificant trend.

Table 2. Results of trend analysis of flood peaks and gauge height in Satluj River Basin.

S. No. Station Trend analysis

Mann-Kendall Linear regression

Flood peak Gauge height Flood peak Gauge height

1. Rampur + (0.72) + (0.48) + (0.70) + (0.34)

2. Suni + (0.84) + (0.71) + (0.29) + (0.23)

3. Kasol + (0.99) + (0.02)* + (0.47) + (0.01)*

*Significance at 95% confidence level. (+) increasing, (-) decreasing.

The trend analysis results (Table 2) of flood peaks and gauge heights by non-parametric Mann-

Kendall and parametric linear regression tests indicate that the flood peaks at all sites i.e. Rampur,

Suni and Kasol show increasing but statistically insignificant trends. The trends in gauge height at

all sites are also showing increasing trend but Kasolis statistically significant at 95% confidence

level.

The trends found by the linear regression were almost similar to the trends found by the Mann-

Kendall test. There were 100% increasing trends in both flood peaks and gauge heights but only

gauge height of Kasol was significant at 95% confidence level. Ironically, the precipitation, coupled

y = -4,1911x + 916,93

R² = 0,0458

y = 21.38x + 2087.

R² = 0.016

0

1000

2000

3000

4000

5000

6000

7000

8000

0

200

400

600

800

1000

1200

1400

Flo

od

pea

ks

(Cu

mecs)

Mo

nso

on

al d

isch

arg

e (C

um

ecs)

Year

Monsoonal discharge Vs flood peaks at Kasol


72 ILNS Volume 46

with increasing temperature may cause further increase in the component of glacier-melt and also

enhance flood peaks which indicate a direct relationship between climate change and annual flood

peak discharges. The river experiences the peak discharge value in July and August which is

essentially contributed by monsoon rainfall in the lower part of basin apart from snow and glacier

meltdown. The correlation between the annual as well as monsoonal flow was negative implying

that decrease in discharge value can not be expected to be associated with decreasing flood peaks.

This may be due to decreasing contribution to flow from melting of snow and glaciers; and

increasing contribution from incessant and spurt rainfall during monsoon season. But the relation

between annual discharge and flood peaks at Kasol site showed exceptional trends.

The fast melting of glaciers, incessant monsoon rainfall and the synchronisation of the discharge

peaks are the main causes of river floods. The impacts of climatic change on the Satluj River Basin

where decreased contribution from glacier melt, irregular precipitation, and increased intensity and

frequency of the flood peaks, likely became obstacles to the sustainable development of a life-

sustaining system. Potential climate change related extreme events could intensify fragility of

mountainous system where human activities lead to increased risk, resulting in greater sensitivity to

climate change. Hydrological analysis of projected future climate change shows considerable

changes in the frequency of peak discharges of rivers (IPCC, 2007b) which indicates future changes

in risk. Damages from weather related disasters are projected to increase, due to a combination of

increasing exposure of people and assets, and expected changes in the global climate.

Rapid and frequent changes in the discharge reduce the resistance of river bank to erosion.

There has been disastrous erosion along the river due to extreme instability of the banks at several

places. The high degree of instability in these areas makes them more vulnerable during flood

season. Increasing flood peaks into river have cut and eroded the banks dangerously which in turn is

threatening many habitations and other infrastructure. National Highway 22 that runs along the river

is getting unstable at several places and the bridges are also under threat. The identification and

quantification of historical peak discharge data provides answers to the questions like whether the

frequency and magnitude of extreme events have increased or not during the past few decades, in

relation to climatic variability. The sensitivity of flood peak variations provides important insight

regarding the responses and vulnerability of different areas to climate change. The influence of

climate change on such hydrologic processes, especially in mountain basins is paramount for

understanding, forecasting and mitigating water related hazards.

5. Conclusions

Trend analysis of historical data concluded that the spatial and temporal variations in flood peak

are due to climate change. The impacts of an intensification of flood peaks, most remarkably in

connection with the possibility of an increasing occurrence of extreme events will adversely affect

the people living downstream of such mountainous river. Studying the past flood peaks will help us

to observe the frequency of occurrence of floods in certain region and to determine whether the

flood peaks in the past have been same with that of the present or whether there is any deviation in

the trend in relation to climate change. Information from trend analysis will be useful in the

planning, development and management of water resources in the study area. Densification of

observation stations, along with detailed study of climate change induced extreme events will

further strengthen the formulation of future strategy for adaptation and mitigation.

Acknowledgements

Authors are thankful to Indian Council of Medical Research (ICMR) and University Grant

Commission (UGC), New Delhi for providing financial assistance in the form of research

fellowship.


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Documents

Trend Analysis of Flood Peaks in Lower Reaches of Satluj